Ph.D. Project: Software Development for MRS module/Clinical Decision Support System

The aim of this project is to create a software solution to integrate Magnetic Resonance Spectroscopy (MRS) data processing within the EU-Project. An important part of the processing of MRS data is the quantification of the metabolites. Since with the MRS technique we are able to detect a fingerprint of the metabolism, it is also important to be able to assess what metabolites are present in which concentrations in the different brain regions. To achieve this quantification, a so called fitting software is needed, which should be designed in this Ph.D. project to become a versatile tool.

The CDS-QuaMRI project:

My Ph.D. project is funded by the EU-project CDS-QuaMRI. The aim of this EU-project is to create a clinical decision support software which connects different Magnetic Resonance Imaging (MRI) modalities to allow a quantitative processing of the data. This software should provide a modality to study the causes and neurological behavior of diseases, which are more difficult to diagnose currently with single modalities. The study shall be tested on patients suffering from major depressive disorder (MDD) and multiple sclerosis (MS).

: provides the arterial spin labeling sequence. This technique can quantify the perfusion (blood distribution) in the brain.

4. : provides methods for post-processing anatomical data and quantification methods for functional-MRI (fMRI) processing. The fMRI searches for activation regions in the brain, observed at resting state or when a given stimuli are used. Of special interest is the functional connectivity of brain regions.

5. in collaboration with the : contributes with methods for MRS data analysis. MRS allows the quantification of the metabolism and hence also of the healthiness state of brain tissue.

6. : aims at developing a technique to classify the multi-modal data, acquired in Berlin and London and processed with the tools of the other sites, to distinguish based on characteristic features healthy subjects from patients. Machine learning will be the technique used for this.

7. : this company will offer a software platform for the multi-modal quantification software. This software will create both interfaces to the MRI hardware, but also provide the software engineering design to integrate the different modalities.

Figure 1:

The project is constructed using several existing software solutions provided in a heterogeneous format from the different sites. Hence the software engineering is rather complex, but it has the advantage of integrating tested algorithms developed during the long term.

My contribution sides on the co-development of the software engineering structure with GyroTools, and restructuring and integrating the existing spectroscopy scripts into the final software solution.

MRS Fitting

In a magnetic field, atoms interact and this interaction creates chemical shifts of the different nuclei within a molecule. This phenomenon is used in MRS and this way we can visualize spectra, which represent peaks or a set of peaks, which reflect the molecular structure and abundance of metabolites in a given volume of interest. The knowledge of the chemical shifts and other properties allow us to distinguish the different metabolites present in the brain. By evaluating the properties, which are seen by the metabolites and solving an optimization problem we can evaluate the concentration of given metabolites. This process is called fitting.

In this fitting process, a good software has to consider artifacts, account for lipid signals, or for so called macromolecular signals, which originate mainly from amino acids.

We both investigate the fitting of 1-dimensional spectra (figure 2.A) and spectra which are resolved in a second dimension, like the J-sLASER spectra (figure 2.B). A long term objective is to fit spectroscopy imaging (MRSI) data, which captures the spatial distribution of the metabolites in the brain. The challenge is to mitigate the worse spectral quality of individual voxels by spatial prior knowledge. Figure 2.C shows how artifacts and lipids can affect the spectral fitting, hence a robust algorithm has to be developed.

Figure 2:

Another aspect investigated in the fitted concentrations is the effects of the origin and composition (gray or white matter richness) of the macromolecular baseline used for fitting (See figure 3).